At present, a virtual data maintenance system is generally used in the cloud computing field to perform failure diagnosis and operations on multitudinous virtual machines, and bottom-layer scheduling records of the virtual machines become an important auxiliary means for diagnosing failures of virtual machines in a cloud system. Generally, because of an internal policy and manual intervention, a virtual machine generates many events, and in a scenario of failure handling, these events are important bases for maintenance personnel to diagnose failures of virtual machines.
In the prior art, the bottom-layer scheduling records of the virtual machines are generally available only in the form of an operation log. Operation types recorded in the operation log include: setting a monitoring alarm threshold; modifying basic configurations of elastic computing; adding, modifying, and deleting a virtual machine specification; releasing, modifying, and deleting a virtual disk image; adding, deleting, and modifying operation and maintenance system (Operations and Maintenance system, OMS) administrator information; powering on/off and restarting a physical server; migrating a virtual machine; creating an ISO virtual machine, and so on. Maintenance personnel are unable to trace service interaction information at the cloud bottom layer of a virtual machine by merely using these operation logs. This is a function deficiency in multi-dimensional maintenance means centering on virtual machines.
In the prior art, if tracing is performed according to operation logs, maintenance personnel are faced with a huge amount of information and unable to mine the service interaction information at the cloud bottom layer and unable to see logical relationships between multiple states and multiple events easily. Therefore, correlation analysis cannot be provided for diagnosing a failure of a virtual machine, and the cause of the failure of the virtual machine cannot be further determined in a comprehensive multi-dimensional manner.